#207 · Primary category: Computer Vision
multi-object-tracker
Multi-object trackers in Python
Project last updated:07/17/26
GitHub Stars
696
Forks
101
Contributors
6
License
MIT
Why we included this project
Multi-object tracking is fiddly: you need to match detections across frames without losing identities, and the classic algorithms each have their own quirks. This library wraps CentroidTracker, IOUTracker, CentroidKF_Tracker, and SORT behind one simple API, so you can test different strategies without rewriting your pipeline. It also includes OpenCV dnn-based detectors like YOLOv3 and SSD MobileNet that feed the trackers, which makes it a handy starting point for a full detect-and-track loop. The examples run on real video, and the code is compact enough to read and modify, so it works well both for learning how trackers behave and for getting a working prototype up quickly.
Articles for this project
No articles for this project yet.
To suggest a topic or contribute an article, contact us.
Related projects in this category
opencv
Open Source Computer Vision Library
RuView
π RuView turns commodity WiFi signals into real-time spatial intelligence, vital sign monitoring, and presence detection — all without a single pixel of video.
PaddleOCR
Turn any PDF or image document into structured data for your AI. A powerful, lightweight OCR toolkit that bridges the gap between images/PDFs and LLMs. Supports 100+ languages.
MinerU
Transforms complex documents like PDFs and Office docs into LLM-ready markdown/JSON for your Agentic workflows.
tesseract
Tesseract Open Source OCR Engine (main repository)